Any discussion of the prior art throughout the specification should in no way be considered as an admission that such prior art is widely known or forms part of the common general knowledge in the field. In particular the references cited throughout the in specification should in no way be considered as an admission that such art is prior art, widely known or forms part of the common general knowledge in the field.
Linear or elongated feature detection is a very important task in the areas of image analysis, computer vision, and pattern recognition. It has a very wide range of applications ranging from retinal vessel extraction, skin hair removal for melanoma detection, and fingerprint analysis in the medical and biometrics area, neurite outgrowth detection and compartment assay analysis in the biotech area as described in Ramm et al., Van de Wouwer et al. and Meijering et al. It is also used for characterizing, tree branches, tree bark, plant roots, and leaf vein/skeleton detection; and in the infrastructure areas for road crack detection, roads and valleys detection in satellite images as described in Fischler et al.
There are a number of techniques in the literature for linear feature detection. Quite a few are aimed at detecting retinal vessels. A recent review of some of the available vessel extraction techniques and algorithms can be found in Kirbas and Quek.
One type of extraction technique, such as that disclosed in Bamberger and Smith, requires a series of directional filters corresponding to the direction of the structures present in the image. Some of the methods employed there include steerable filters as described in Gauch and Pier, 2D matched filters as described in Chaudhuri et al., maximum gradient profiles as described in Colchester et al., and directional morphological filtering such as that used by Soille and Talbot. This type of techniques can be termed as either template or model-based and tend to be slow.
Another approach to linear feature detection uses the classical gradient/curvature or Hessian-based detectors. These techniques include the use of thin nets or crest lines as described in Monga et al., and ridges as described in Lang et al., Eberly, and Gauch and Pizer, and are also generally computationally expensive.
A further method of linear feature detection involves the use of tracking techniques such as stick growing as described in Nelson, and tracking as described in Can et al. and Tolias and Panas. The tracking based approach requires initial locations of linear features, which typically need user intervention. For example, methods using edge operators to detect pairs of edges and graph searching techniques to find the centrelines of vessel segments are presented in Fleagle et al. and Sonka et al. These methods require the user to identify the specific areas of interest. There are also other related techniques which are termed edge or “roof” based, such as those described in Zhou et al., Nevatia and Babu, and Canny. Other known feature detection algorithms include pixel classification using neural network scheme through supervised training as described in Staal, S-Gabor filter and deformable splines as described in Klein, and mathematical morphology as described in Zana and Klein.